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https://github.com/kwame-mintah/aws-lambda-model-deployment
A lambda to deploy a machine learning model inference via serverless. Will create a new model, endpoint configuration and serverless endpoint configuration within AWS SageMaker.
https://github.com/kwame-mintah/aws-lambda-model-deployment
aws aws-lambda model-deployment python python311
Last synced: about 1 month ago
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A lambda to deploy a machine learning model inference via serverless. Will create a new model, endpoint configuration and serverless endpoint configuration within AWS SageMaker.
- Host: GitHub
- URL: https://github.com/kwame-mintah/aws-lambda-model-deployment
- Owner: kwame-mintah
- Created: 2024-02-25T10:19:43.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-08-06T20:46:41.000Z (5 months ago)
- Last Synced: 2024-08-06T23:58:00.083Z (5 months ago)
- Topics: aws, aws-lambda, model-deployment, python, python311
- Language: Python
- Homepage:
- Size: 117 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
README
# AWS Lambda Model Deployment
[](https://www.python.org/downloads/release/python-3121/)
[](https://github.com/kwame-mintah/aws-lambda-model-deployment/actions/workflows/bump-repository-version.yml)
[](https://github.com/kwame-mintah/aws-lambda-model-deployment/actions/workflows/push-docker-image-to-aws-ecr.yml)
[](https://github.com/kwame-mintah/aws-lambda-model-deployment/actions/workflows/run-python-linter.yml)
A lambda to deploy a model via serverless after training has completed. The model artifacts will be used to create a new model,
endpoint configuration and serverless endpoint configuration within SageMaker.
This repository does not create other necessarily resources within AWS, this is created via Terraform found here [terraform-aws-machine-learning-pipeline](https://github.com/kwame-mintah/terraform-aws-machine-learning-pipeline).
For more details on the entire flow and how this lambda is deployed, see [aws-automlops-serverless-deployment](https://github.com/kwame-mintah/aws-automlops-serverless-deployment).
# Flowchart
The [diagram below](https://mermaid.js.org/syntax/flowchart.html#flowcharts-basic-syntax) demonstrates what happens when the lambda is trigger, when a new `.tar.gz` object has been uploaded to the S3 Bucket.
```mermaid
graph LR
S0(Start)
T1(Create SageMaker model with output artifact)
T2(Create Endpoint configuration using xgboost)
T3(Create Serverless endpoint)
T4(Send message to Model Evaluation SQS)
E0(End)
S0-->T1
T1-->T2
T2-->T3
T3-->T4
T4-->E0
```
# Notice
Please note because the SageMaker endpoint deployed is serverless, means [`DataCaptureConfig`](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture-endpoint.html),
is not supported and will have to rely on CloudWatch logs as per official [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints-monitoring.html).
Because endpoint(s) and endpoint configuration(s) are not created via Terraform, these will not be deleted when
destroying the environment and will need to be deleted manually or programmatically, for example if the new model is
not perform as expected, delete all related configurations created for inference.
If Terraform has destroyed all related infrastructure related to SageMaker, for example S3 bucket containing model output
has been deleted, then you are unable to invoke the endpoint because the model no longer exists.
## Development
### Dependencies
- [Python](https://www.python.org/downloads/release/python-3121/)
- [Docker for Desktop](https://www.docker.com/products/docker-desktop/)
- [Amazon Web Services](https://aws.amazon.com/?nc2=h_lg)
## Usage
1. Build the docker image locally:
```shell
docker build --no-cache -t model_deployment:local .
```
2. Run the docker image built:
```shell
docker run --platform linux/amd64 -p 9000:8080 model_deployment:local
```
3. Send an event to the lambda via curl:
```shell
curl "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}'
```
Example AWS S3 event received
```json
{
"Records": [
{
"eventVersion": "2.0",
"eventSource": "aws:s3",
"awsRegion": "us-east-1",
"eventTime": "1970-01-01T00:00:00.000Z",
"eventName": "ObjectCreated:Put",
"userIdentity": { "principalId": "EXAMPLE" },
"requestParameters": { "sourceIPAddress": "127.0.0.1" },
"responseElements": {
"x-amz-request-id": "EXAMPLE123456789",
"x-amz-id-2": "EXAMPLE123/5678abcdefghijklambdaisawesome/mnopqrstuvwxyzABCDEFGH"
},
"s3": {
"s3SchemaVersion": "1.0",
"configurationId": "testConfigRule",
"bucket": {
"name": "example-bucket",
"ownerIdentity": { "principalId": "EXAMPLE" },
"arn": "arn:aws:s3:::example-bucket"
},
"object": {
"key": "2024-02-23/output/xgboost-2024-02-23-18-04-06-024/output/model.tar.gz",
"size": 1024,
"eTag": "0123456789abcdef0123456789abcdef",
"sequencer": "0A1B2C3D4E5F678901"
}
}
}
]
}
```
## GitHub Action (CI/CD)
The GitHub Action "🚀 Push Docker image to AWS ECR" will checkout the repository and push a docker image to the chosen AWS ECR using
[configure-aws-credentials](https://github.com/aws-actions/configure-aws-credentials/tree/v4.0.1/) action. The following repository secrets need to be set:
| Secret | Description |
|--------------------|------------------------------|
| AWS_REGION | The AWS Region. |
| AWS_ACCOUNT_ID | The AWS account ID. |
| AWS_ECR_REPOSITORY | The AWS ECR repository name. |